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Marco Tedesco - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Approaches for Snow Depth Retrieval From Spaceborne Microwave Brightness Temperature
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: Marco Tedesco, Rolf H. Reichle, A. Low, Thorsten Markus, James L. FosterAbstract:Snow depth (SD) can be retrieved from spaceborne data through linear regression against the microwave Brightness Temperature difference between 19 and 37 GHz (or similar frequencies). Other methods use snow physical and/or snow electromagnetic (EM) models to estimate SD. Here, we introduce novel retrieval approaches that dynamically combine ancillary SD information (e.g., from snow physical models driven with surface meteorological data) with established algorithms based on regression or EM modeling. The basic idea is to recalibrate regression coefficients (or the effective grain size in the case of EM models) once per week in a simple data assimilation scheme. SD is retrieved from Special Sensor Microwave Imager Brightness Temperature data and evaluated against in situ observations from 37 stations throughout the Northern Hemisphere. As expected, the SD retrievals perform better with (weekly) ancillary SD inputs from in situ measurements (not used in validation) than with (weekly) ancillary SD inputs from snow physical modeling. The best results are obtained with the regression-based approach using dynamically recalibrated coefficients and ancillary SD inputs from in situ observations (rmse = 6 cm). The regression approach still performs better with the time average of the dynamic coefficients (rmse = 8 cm) than with standard literature values based on climatology ("REGR-CLIM"; rmse = 50 cm). For SD retrieval with an EM model, we obtain results comparable to REGR-CLIM (rmse = 44 cm). Driving the novel regression approaches with SD estimates from snow physical modeling still results in improvements over REGR-CLIM for all approaches (rmse = 15 cm). Comparable SD estimates are obtained from the snow physical model alone.
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assessment and development of snowmelt retrieval algorithms over antarctica from k band spaceborne Brightness Temperature 1979 2008
Remote Sensing of Environment, 2009Co-Authors: Marco TedescoAbstract:Abstract Results from several previously published algorithms for wet snow detection in Antarctica from K-band spaceborne Brightness Temperature are compared and evaluated vs. estimates of wet snow conditions from ground measurements. In addition, a new physically-driven algorithm, in which the detectable liquid water content is assumed constant, is proposed and assessed. All algorithms are also evaluated by analyzing their results during collapses of ice shelves. Two algorithms are selected for deriving updated trends of melting index (MI, the number of melting days times the area subject to melting) between 1979 and 2008 over the whole Antarctica and at sub-continental scales. In the first algorithm wet snow is identified when Brightness Temperature exceeds the mean of winter Brightness Temperature plus 30 K and the second is the new model-based approach described here. Both negative and positive MI trends are obtained, depending on the algorithm used. A high number of melting days (up to 100 days) are detected over the Wilkins ice shelf, the Peninsula and the George VI ice shelf. Over East Antarctica, the West and Amery ice shelves are subject to melting for a maximum of approximately 50 days. Positive trends of number of melting days are detected over most of the West Antarctica, with peak values up to 1.2 days/year over the Larsen C ice shelf, 1.8 days/year over the George VI ice shelf and 0.55 days/year over the Wilkins ice shelf area. The correlation between MI values and December–January (DJ) averaged air/surface Temperature over selected locations show values ranging between ∼ 0.8 and ∼ 0.4. Results suggest that a 1 °C increase in the monthly averaged DJ air/surface Temperature corresponds to an average MI increase of approximately 2·106 × km2 × day.
Ghislain Picard - One of the best experts on this subject based on the ideXlab platform.
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modeling l band Brightness Temperature at dome c in antarctica and comparison with smos observations
IEEE Transactions on Geoscience and Remote Sensing, 2015Co-Authors: Marion Leducleballeur, Ghislain Picard, Laurent Arnaud, Arnaud Mialon, Eric Lefebvre, Philippe Possenti, Yann KerrAbstract:Two electromagnetic models were used to simulate snow emission at L-band from in situ measurements of snow properties collected at Dome C in Antarctica. Two different approaches were used: one based on the radiative transfer theory and the other on the wave approach. The soil moisture ocean salinity (SMOS) satellite observations performed at 1.4 GHz (21 cm) were used to check the validity of these models. Model results based on the wave approach were in good agreement with SMOS observations, particularly for incidence angles lower than 55°. Comparisons suggest that the wave approach is more suitable to simulate Brightness Temperature at L-band than the transfer radiative theory, because interference between the layers of the snowpack is better taken into account. The model based on the wave approach was then used to investigate several L-band characteristics at Dome C. The emission e-folding depth, i.e., 67% of the signal, was estimated at 250 m, and 99% of the signal emanated from the top 900 m. L-band Brightness Temperature is only slightly affected by seasonal variations in surface Temperature, confirming the high temporal stability of snow emission at low frequency. Sensitivity tests showed that good knowledge of density variability in the snowpack is essential for accurate simulations in L-band.
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influence of meter scale wind formed features on the variability of the microwave Brightness Temperature around dome c in antarctica
The Cryosphere, 2013Co-Authors: Ghislain Picard, Alain Royer, Laurent Arnaud, Michel FilyAbstract:Abstract. Space-borne passive microwave radiometers are widely used to retrieve information in snowy regions by exploiting the high sensitivity of microwave emission to snow properties. For the Antarctic Plateau, many studies presenting retrieval algorithms or numerical simulations have assumed, explicitly or not, that the subpixel-scale heterogeneity is negligible and that the retrieved properties were representative of whole pixels. In this paper, we investigate the spatial variations of Brightness Temperature over a range of a few kilometers in the Dome C area. Using ground-based radiometers towed by a vehicle, we collected Brightness Temperature at 11, 19 and 37 GHz at horizontal and vertical polarizations along transects with meter resolution. The most remarkable observation was a series of regular undulations of the signal with a significant amplitude reaching 10 K at 37 GHz and a quasi-period of 30–50 m. In contrast, the variability at longer length scales seemed to be weak in the investigated area, and the mean Brightness Temperature was close to SSM/I and WindSat satellite observations for all the frequencies and polarizations. To establish a link between the snow characteristics and the microwave emission undulations, we collected detailed snow grain size and density profiles at two points where opposite extrema of Brightness Temperature were observed. Numerical simulations with the DMRT-ML microwave emission model revealed that the difference in density in the upper first meter explained most of the Brightness Temperature variations. In addition, we found that these variations of density near the surface were linked to snow hardness. Patches of hard snow – probably formed by wind compaction – were clearly visible and covered as much as 39% of the investigated area. Their Brightness Temperature was higher than in normal areas. This result implies that the microwave emission measured by satellites over Dome C is more complex than expected and very likely depends on the year-to-year areal proportion of the two different types of snow.
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Modeling time series of microwave Brightness Temperature in Antarctica
Journal of Glaciology, 2009Co-Authors: Ghislain Picard, Ludovic Brucker, Michel Fily, Hubert Gallée, Gerhard KrinnerAbstract:This paper aims to interpret the temporal variations of microwave Brightness Temperature (at 19 and 37 GHz and at vertical and horizontal polarizations) in Antarctica using a physically based snow dynamic and emission model (SDEM). SDEM predicts time series of top-of-atmosphere Brightness Temperature from widely available surface meteorological data (ERA-40 re-analysis). To do so, it successively computes the heat flux incoming the snowpack, the snow Temperature profile, the microwaves emitted by the snow and, finally, the propagation of the microwaves through the atmosphere up to the satellite. Since the model contains several parameters whose value is variable and uncertain across the continent, the parameter values are optimized for every 50 km × 50 km pixel. Simulation results show that the model is inadequate in the melt zone (where surface melting occurs on at least a few days a year) because the snowpack structure and its temporal variations are too complex. In contrast, the accuracy is reasonably good in the dry zone and varies between 2 and 4 K depending on the frequency and polarization of observations and on the location. At the Antarctic scale, the error is larger where wind is usually stronger, suggesting either that meteorological data are less accurate in windy regions or that some neglected processes (e.g. windpumping, surface scouring) are important. At Dome C, in calm conditions, a detailed analysis shows that most of the error is due to inaccuracy of the ERA-40 air Temperature (∼2 K). Finally, the paper discusses the values of the optimized parameters and their spatial variations across the Antarctic.
Alexander Loew - One of the best experts on this subject based on the ideXlab platform.
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Brightness Temperature and soil moisture validation at different scales during the smos validation campaign in the rur and erft catchments germany
IEEE Transactions on Geoscience and Remote Sensing, 2013Co-Authors: Carsten Montzka, Juha Kainulainen, Heye Bogena, Lutz Weihermuller, Francois Jonard, C Bouzinac, Jan E Balling, Alexander Loew, J T Dallamico, E RouheAbstract:The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009 and delivers now Brightness Temperature and soil moisture products over terrestrial areas on a regular three-day basis. In 2010, several airborne campaigns were conducted to validate the SMOS products with microwave emission radiometers at L-band (1.4 GHz). In this paper, we present results from measurements performed in the Rur and Erft catchments in May and June 2010. The measurement sites were situated in the very west of Germany close to the borders to Belgium and The Netherlands. We developed an approach to validate spatial and temporal SMOS Brightness Temperature products. An area-wide Brightness Temperature reference was generated by using an area-wide modeling of top soil moisture and soil Temperature with the WaSiM-ETH model and radiative transfer calculation based on the L-band Microwave Emission of the Biosphere model. Measurements of the airborne L-band sensors EMIRAD and HUT-2D on-board a Skyvan aircraft as well as ground-based mobile measurements performed with the truck mounted JULBARA L-band radiometer were analyzed for calibration of the simulated Brightness Temperature reference. Radiative transfer parameters were estimated by a data assimilation approach. By this versatile reference data set, it is possible to validate the spaceborne Brightness Temperature and soil moisture data obtained from SMOS. However, comparisons with SMOS observations for the campaign period indicate severe differences between simulated and observed SMOS data.
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analysis of smos Brightness Temperature and vegetation optical depth data with coupled land surface and radiative transfer models in southern germany
Hydrology and Earth System Sciences, 2012Co-Authors: F Schlenz, J T Dallamico, Wolfram Mauser, Alexander LoewAbstract:Soil Moisture and Ocean Salinity (SMOS) L1c Brightness Temperature and L2 optical depth data are anal- ysed with a coupled land surface (PROMET) and radiative transfer model (L-MEB). The coupled models are validated with ground and airborne measurements under contrasting soil moisture, vegetation and land surface Temperature con- ditions during the SMOS Validation Campaign in May and June 2010 in the SMOS test site Upper Danube Catchment in southern Germany. The Brightness Temperature root-mean- squared errors are between 6 K and 9 K. The L-MEB param- eterisation is considered appropriate under local conditions even though it might possibly be further optimised. SMOS L1c Brightness Temperature data are processed and analysed in the Upper Danube Catchment using the coupled models in 2011 and during the SMOS Validation Campaign 2010 together with airborne L-band Brightness Temperature data. Only low to fair correlations are found for this comparison (R between 0.1-0.41). SMOS L1c Brightness Temperature data do not show the expected seasonal behaviour and are pos- itively biased. It is concluded that RFI is responsible for a considerable part of the observed problems in the SMOS data products in the Upper Danube Catchment. This is consistent with the observed dry bias in the SMOS L2 soil moisture products which can also be related to RFI. It is confirmed that the Brightness Temperature data from the lower SMOS look angles and the horizontal polarisation are less reliable. This information could be used to improve the Brightness temper- ature data filtering before the soil moisture retrieval. SMOS L2 optical depth values have been compared to modelled data and are not considered a reliable source of information about vegetation due to missing seasonal behaviour and a very high mean value. A fairly strong correlation between SMOS L2 soil moisture and optical depth was found (R = 0.65) even though the two variables are considered independent in the study area. The value of coupled models as a tool for the analysis of passive microwave remote-sensing data is demon- strated by extending this SMOS data analysis from a few days during a field campaign to a longer term comparison.
I Corbella - One of the best experts on this subject based on the ideXlab platform.
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minimization of image distortion in smos Brightness Temperature maps over the ocean
IEEE Geoscience and Remote Sensing Letters, 2012Co-Authors: F Torres, I Corbella, N Duffo, Jerome Gourrion, Jordi Font, Manuel MartinneiraAbstract:Soil Moisture and Ocean Salinity (SMOS) Brightness Temperature synthesized images are obtained after a comprehensive error correction procedure that takes into account both on-ground and in-flight calibration measurements. However, the final images are still contaminated by small, although nonnegligible, spatial errors: the so-called pixel bias. Since spatial errors in the 2-D SMOS images are not zero mean along track, these errors produce clearly visible artifacts aligned to this direction. Fortunately, spatial errors have been found to be very stable and can be minimized once the image distortion pattern is properly measured by observing a target at a uniform Brightness Temperature distribution. This letter describes the procedure to compute a multiplicative mask that largely reduces spatial errors over the ocean. Preliminary results to assess the mask performance are also presented by computing the reduction of the rms spatial error for a number of targets selected to have significant temporal and geographical diversity.
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Brightness Temperature retrieval methods in synthetic aperture radiometers
IEEE Transactions on Geoscience and Remote Sensing, 2009Co-Authors: I Corbella, F Torres, N Duffo, Adriano Camps, Merce VallllosseraAbstract:Brightness-Temperature retrieval techniques for synthetic aperture radiometers are reviewed. Three different approaches to combine measured visibility and antenna Temperatures, along with instrument characterization data, into a general equation to invert are presented. Discretization and windowing techniques are briefly discussed, and formulas for reciprocal grids using rectangular and hexagonal samplings are given. Two known techniques are used to invert the equation, namely, inverse Fourier transform and G -matrix pseudoinverse. The proposed preprocessing approaches combined with these two inversion methods are implemented with real data measured by an airborne Y-shaped interferometric radiometer over land and water, and are compared. The images indicate that best results are obtained when inverting an incremental visibility obtained after substracting a term that includes the individual antenna Temperatures, the physical Temperatures of the receivers, and a flat-target response directly measured from cold-sky looks.
Michel Fily - One of the best experts on this subject based on the ideXlab platform.
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influence of meter scale wind formed features on the variability of the microwave Brightness Temperature around dome c in antarctica
The Cryosphere, 2013Co-Authors: Ghislain Picard, Alain Royer, Laurent Arnaud, Michel FilyAbstract:Abstract. Space-borne passive microwave radiometers are widely used to retrieve information in snowy regions by exploiting the high sensitivity of microwave emission to snow properties. For the Antarctic Plateau, many studies presenting retrieval algorithms or numerical simulations have assumed, explicitly or not, that the subpixel-scale heterogeneity is negligible and that the retrieved properties were representative of whole pixels. In this paper, we investigate the spatial variations of Brightness Temperature over a range of a few kilometers in the Dome C area. Using ground-based radiometers towed by a vehicle, we collected Brightness Temperature at 11, 19 and 37 GHz at horizontal and vertical polarizations along transects with meter resolution. The most remarkable observation was a series of regular undulations of the signal with a significant amplitude reaching 10 K at 37 GHz and a quasi-period of 30–50 m. In contrast, the variability at longer length scales seemed to be weak in the investigated area, and the mean Brightness Temperature was close to SSM/I and WindSat satellite observations for all the frequencies and polarizations. To establish a link between the snow characteristics and the microwave emission undulations, we collected detailed snow grain size and density profiles at two points where opposite extrema of Brightness Temperature were observed. Numerical simulations with the DMRT-ML microwave emission model revealed that the difference in density in the upper first meter explained most of the Brightness Temperature variations. In addition, we found that these variations of density near the surface were linked to snow hardness. Patches of hard snow – probably formed by wind compaction – were clearly visible and covered as much as 39% of the investigated area. Their Brightness Temperature was higher than in normal areas. This result implies that the microwave emission measured by satellites over Dome C is more complex than expected and very likely depends on the year-to-year areal proportion of the two different types of snow.
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Modeling time series of microwave Brightness Temperature in Antarctica
Journal of Glaciology, 2009Co-Authors: Ghislain Picard, Ludovic Brucker, Michel Fily, Hubert Gallée, Gerhard KrinnerAbstract:This paper aims to interpret the temporal variations of microwave Brightness Temperature (at 19 and 37 GHz and at vertical and horizontal polarizations) in Antarctica using a physically based snow dynamic and emission model (SDEM). SDEM predicts time series of top-of-atmosphere Brightness Temperature from widely available surface meteorological data (ERA-40 re-analysis). To do so, it successively computes the heat flux incoming the snowpack, the snow Temperature profile, the microwaves emitted by the snow and, finally, the propagation of the microwaves through the atmosphere up to the satellite. Since the model contains several parameters whose value is variable and uncertain across the continent, the parameter values are optimized for every 50 km × 50 km pixel. Simulation results show that the model is inadequate in the melt zone (where surface melting occurs on at least a few days a year) because the snowpack structure and its temporal variations are too complex. In contrast, the accuracy is reasonably good in the dry zone and varies between 2 and 4 K depending on the frequency and polarization of observations and on the location. At the Antarctic scale, the error is larger where wind is usually stronger, suggesting either that meteorological data are less accurate in windy regions or that some neglected processes (e.g. windpumping, surface scouring) are important. At Dome C, in calm conditions, a detailed analysis shows that most of the error is due to inaccuracy of the ERA-40 air Temperature (∼2 K). Finally, the paper discusses the values of the optimized parameters and their spatial variations across the Antarctic.